Wideband CDMA (WCDMA) is one of the promising radio access technologies for IMT-2000. The objective of a WCDMA system is to provide users a radio access link to services comparable to those currently offered by fixed networks, resulting in a seamless convergence of both fixed and mobile services. The WCDMA is designed to integrate different types of services, such as voice, data, image, and compressed video where heterogeneous QoS requirements, such as transmission rate, delay, and bit error rate for these differentiated services will be supported. Therefore, an adequate radio resource control (RRC) is required to enhance the spectrum utilization while meeting those heterogeneous QoS requirements.
The physical layer and the MAC specifications for WCDMA are defined by 3GPP [16]-[17]. The WCDMA has two types of uplink dedicated physical channels (DPCHs): the uplink dedicated physical data channel (DPDCH) and the uplink dedicated physical control channel (DPCCH). A DPDCH is used to carry data generated by layer 2 and above, and a DPCCH is used to carry layer 1 control information. Each connection is allocated a DPCH including one DPCCH and zero, one, or several DPDCHs. The channel is defined in a frame-based structure, where the frame length Tf=10 ms is divided into 15 slots with length Tslot=2560 chips, each slot corresponding to one power control period. Hence, the power control frequency is 1500 Hz. The spreading factor (SF) for DPDCH can vary between 4˜256 by SF=256/2k, k=0, 1 . . . 6, carrying 10×2k bits per slot, and the SF for DPCCH is fixed at 256, carrying 10 bits per slot.
In addition, a common physical channel, named physical random access channel (PRACH), is defined to carry uplink random access burst(s). The PRACH carries random access bursts and short packets in the uplink. Based on a slotted ALOHA approach, a UE (user equipment) can start the transmission of a random access burst at a number of well-defined time-offsets, named access slots. There are 15 access slots per two frames and they are spaced 5120 chips apart. A random access burst contains a preamble part and a message part. The message part has a length of 10 ms and it is split into a data part and a control part similar to the uplink DPDCH and DPCCH, respectively. Note that SF of the data part is variable between 256/2k, k=0,1,2,3, and SF of the control part is fixed at 256.
Two types of WCDMA services are considered: real-time service as type-1 and non-real-time service as type-2. The WCDMA communication system provides connection-oriented transmission for real-time traffic and best-effort transmission rate allocation for non-real-time traffic, as the service discipline adopted in [1]. To guarantee the timely constraint of real-time service, a UE always holds a DPCH while it transmits real-time packets regardless of the variation of the required transmission rate. The real-time UE may generate variable rate information whose characteristics are indicated in its request profile. On the other hand, a UE should contend for the reservation of a DPCH to transmit a burst of non-real-time packets. And the UE will release the DPCH immediately after the burst of data is completely transmitted. The non-real-time data are transmitted burst by burst.
When a UE has data to transmit, it first sends its request embedded in a random access burst and transmitted via PRACH. After the base station receives the new request, the admissible transmission rate is evaluated. Due to the service requirements, the RRC performs two different kinds of decision. For a real-time request, the request will be accepted or rejected. On the other hand, for a non-real-time request, an appropriate transmission rate will be allocated. A non-real-time request specifies the range of the required transmission rates for itself, and would be blocked if the WCDMA system cannot provide a suitable transmission rate to satisfy its required transmission rate.
The transmission power of a physical channel should be adjusted according to its spreading factor, coding scheme, rate matching attributes, and BER requirement. It is assumed that all physical channels adopt the same coding scheme and have the same rate matching attributes and BER requirement. Therefore, power allocation for a physical channel is simply dependent of its spreading factor and in inverse proportion [18]. Since each UE determines its up-link transmission power in a distributed manner, the total received interference power at base station is time-varying. For operational stability, the transmission power is determined under the consideration of maximal allowed interference power. In this way, for WCDMA systems, the SIR-based power control scheme, which is specified by 3GPP, is equivalent to the strength-based power control scheme. The complexity of the multi-rate transmission control is reduced and operates regardless of the varying of the received interference.
The multi-rate transmission control in the WCDMA system is to assign it power and processing gain for different service requests so as to maximize the system capacity and to fulfill the users' satisfaction and QoS requirements. In [1], Choi and Shin proposed an uplink CDMA system architecture to provide diverse QoS for heterogeneous traffics: one is real-time (class-I) traffic and the other is non-real-time (class-II) traffic. They theoretically derived the admission region of real-time connections, transmission power allocation, and the optimum target signal-to-interference ratio of non-real-time traffic so as to maximize the system throughput and satisfy the predefined QoS of heterogeneous traffic.
There is no absolute number of maximum available channels in the WCDMA system because WCDMA system is interference-limited. Its capacity is affected by multiple access interference (MAI), which is a function of the number of active users, the users' location, the channel impairments, and heterogeneous QoS requirements. Many researches for CDMA capacity estimation are based on MAI and other considerations [2]-[4]. In [2], a single-service CDMA network with respect to MAI caused by users in the same and adjacent cells was studied. In [3], Huang and Baklawa investigated the uplink performance of a slotted direct sequence CDMA (DS-CDMA) system providing voice and data services. A log-normally distributed MAI model was proposed to estimate the remaining capacity in the CDMA system, where its mean and variance were given by a function of the number of users, and the mean and variance of each service type.
However, in multimedia mobile communication environments, the measured MAI value may not be stationary, and it is also affected by user locations and service profiles. In [4], Kim and Honig studied the resource allocation for multiple classes of traffic in a single cell DS-CDMA system. A joint optimization was investigated over the power and the processing gain of the multiple classes to determine flexible resource allocation for each user subject to QoS constraints.
Shin, Cho and Sung proposed an interference-based channel assignment scheme for DS-CDMA cellular systems [5]. A channel is assigned if the interference is less than an allowed level, which is determined by the network, subject to the QoS constraints. Instead of a fixed system capacity, this interference-based scheme can adaptively assign a channel according to the actual system capacity such that the system utilization and the grade of service can be improved. The interference-based scheme was further extended to call admission control (CAC) in multimedia CDMA cellular systems [6]. A mathematical model was developed to determine the outage limits of a multiple-service CDMA system and to achieve the maximum aggregated capacity for different system parameters.
Maximizing system capacity (revenue) while meeting QoS constraints suggests a constrained semi-Markov decision process (SMDP) [7],[8]. The SMDP has successfully applied to many network control problems; however, it requires extremely large state space to model these problems exactly. Consequently, the numerical computation is intractable due to the sake of dimensionality. Also, a prior knowledge of state transition probabilities is required. Alternatively, many researchers turned to use the reinforcement learning (RL) algorithms to solve the large state space problems [9]-[12]. The most obvious advantage of RL algorithm is that it could obtain an optimal solution from the on-line operation if the RL algorithm is converged. Also, it does not require a prior knowledge of state transition probabilities.